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[attachment=5462]
Wavelet Based Palmprint Authentication System
ABSTRACT
Palm print based personal verification has quickly entered the biometric family due to its ease of acquisition, high user acceptance and reliability. This paper proposes a palm print based identification system using the textural information, employing different wavelet transforms. The transforms employed have been analyzed for their individual as well as combined performances at feature level. The wavelets used for the analysis are Biorthogonal, Symlet and Discrete Meyer. The analysis of these wavelets is carried out on 500 images, acquired through indigenously made image acquisition sys-tem. 500 palm print obtained from 50 users with 10 samples each have been collected over a period of six months and have been evaluated for the performance of the proposed system. The experimental results obtained from the data have demon-strated the feasibility of the proposed system by exhibiting Genuine Acceptance Rate, GAR of 97.12%.
INTRODUCTION
Biometrics based personal identification is getting wide acceptance in the networked society, replacing passwords and keys due to its reliability, uniqueness and the ever in-creasing demand of security. Common modalities being used are fingerprint and face but for face authentication people are still working with the problem of pose and illu-mination invariance where as fingerprint does not have a good psychological effect on the user because of its wide use in crime investigations. If any biometric modality is to succeed in the future it should have the traits like unique-ness, accuracy, richness, ease of acquisition, reliability and above all user acceptance. Palm print based personal identi-fication is a new biometric modality which is getting wide acceptance and has all the necessary traits to make it a part of our daily life. This paper investigates the use of palm print for personal identification using combination of dif-ferent wavelets. Palmprint not only has the unique informa-tion available as on the fingerprint but has far more amount of details in terms of principal lines, wrinkles and creases. Moreover it can easily be combined with hand shape bio metric so as to form a highly accurate and reliable biometric based personal identification system. Palmprint based personal verification has become an in-creasingly active research topic over the years. The Palm-print is rich in information and has been analyzed for dis-criminating features like principal lines. The results reported where wavelet transform has been used for feature extraction has motivated us to investigate the effectiveness of using combination of multiple wavelets for the textural analysis of palmprint.
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PalmPrint Authentication
ABSTRACT
BY:
Arjit Agrawal
Abhishek Patel
Ashish Gupta
Introduction
Palm print recognition inherently implements many of the same matching characteristics that have allowed fingerprint recognition to be one of the most well-known and best publicized biometrics. Both palm and finger biometrics are represented by the information presented in a friction ridge impression.
Because fingerprints and palms have both uniqueness and permanence, they have been used for over a century as a trusted form of identification .
http://studentbank.in/report-finger-prin...ull-report
http://studentbank.in/report-fingerprint-authentication
Posts: 2,300
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Palmprint Authentication System
BY
Arjit Agrawal (0703010025)
Abhishek Patel (0703010007)
Ashish Gupta (0703010029)
Submitted to the Department of Computer Science
in partial fulfillment of the requirements
for the degree of
Bachelor of Technology
ABSTRACT
Automated personal authentication using biometric features is getting
more and more popular for solving the security problems. A new branch of
biometric technology, palmprint authentication, has attracted increasing amount
of attention because palmprints are abundant of line features and thus low resolution images can be used. In this paper, we propose a new approach for palmprint feature extraction, template representation and matching. we present a novel biometric authentication system to identify a person's identity by his/her palmprint. In contrast to existing palmprint systems for criminal applications, the proposed system targets at the civil applications, which require identifying a person in a large database with high accuracy in real-time. The system is constituted by four major components: User Interface Module, Acquisition Module, Recognition Module and External Module. More than 7,000 palmprint images have been collected to test the performance of the system.
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Wavelet Based Palmprint Authentication System
Abstract:
Palmprint is biometric family due to its ease of acquisition, high user acceptance and reliability.
The transforms employed have been analyzed for their individual as well as combined performances at feature level.
The wavelets used for the analysis are Biorthogonal, Symlet and Discrete Meyer.
INTRODUCTION:
Biometrics based personal identification is getting wide acceptance replacing passwords and keys due to its reliability, uniqueness and the ever in-creasing demand of security.
If any biometric is to succeed in the future it should have the traits like uniqueness, accuracy, richness, ease of acquisition, reliability and above all user acceptance.
The Palm-print is rich in information and has been analyzed for discriminating features like where wavelet transform has been used for feature extraction .
Unfortunately, passport, keys, access cards can be lost, duplicated, stolen, or forgotten; and password, secret codes, and personal identification numbers (PINs) can easily be forgotten, Compromised, shared, or observed.
A biometric is a unique, measurable characteristic or trait of a human being for automatically recognizing or verifying identity.
IMAGE ACQUISITION
There are two types of systems available for capturing the palmprint .
Scanners and the pegged systems.
Scanners are hygienically not safe whereas the pegged systems cause considerable inconvenience to the user.
These systems suffer from low user acceptability.
IMAGE REGISTRATION
The acquired color (RGB) parameters of Palmprint are changed to HSI parameters.
Palmprint has been analyzed for its texture using the gray level or intensity values, I among the HSI values.
The gray level images are normalized and thresholded to get a binary image.
The longest line in a palm passes through the middle finger, and any rotation is considered with reference to this line.
FEATURE EXTRACTION AND CLASSIFICATION
The obtained registered palm print image has been analysed for its texture using different symmetrical wavelet families
The palm print region 256x256 has been decom-posed into three scales for each wavelet type.
The selected wavelets have been analyzed for their individual performance by formulating similar energy based feature vectors of length 27, using 9 levels decomposition.
Normalized energy:
The additional energy for performing certain computation-intensive jobs in each system as the energy unit (EU).
The EU for the three systems we studied is between 8 and10µ Joules.
The absolute energy figure for an event varied slightly from day to day.
Concept of palm just like finger:
Palm identification, just like fingerprint identification, is based on the aggregate of information presented in a friction ridge impression.
A fingerprint or palm print appears as a series of dark lines and represents the high, peaking portion of the friction ridged skin .
while the valley between these ridges appears as a white space and is the low, shallow portion of the friction ridged skin.
Friction ridges do not always flow continuously throughout a pattern and often result in specific characteristics such as ending ridges or dividing ridges and dots.
A palm recognition system is designed to interpret the flow of the overall ridges to assign a classification and then extract the minutiae detail
Texture analysis:
The image of a wooden surface is not uniform but contains variations of intensities which form certain repeated patterns called visual texture.
A region in an image has a constant texture if a set of local statistics or other local properties of the picture function are constant, slowly varying, or approximately periodic.
WAVELETS Fourier analysis
Fourier analysis, which breaks down a signal into constituent sinusoids of different frequencies.
Fourier analysis has a serious drawback. In transforming to the frequency domain, time information is lost.
The signal properties do not change much over time that is, if it is what is called a stationary signal this drawback isn’t very important.
Short-Time Fourier analysis:
The Fourier transform to analyze only a small section of the signal at a time a technique called windowing the signal.
The STFT represents a sort of compromise between the time- and frequency-based views of a signal.
you can only obtain this information with limited precision, and that precision is determined by the size of the window.
Wavelet Analysis
Wavelet analysis allows the use of long time intervals where we want more precise low-frequency information, and shorter regions where we want high-frequency information.
wavelet analysis does not use a time-frequency region, but rather a time-scale region.
What Is Wavelet Analysis?
A wavelet is a waveform of effectively limited duration that has an average value of zero.
Compare wavelets with sine waves, which are the basis of Fourier analysis
Fourier analysis consists of breaking up a signal into sine waves of various frequencies.
It also makes sense that local features can be described better with wavelets that have local extent.
The Discrete Wavelet Transform:
Calculating wavelet coefficients at every possible scale is a fair amount of work, and it generates an awful lot of data.
For many signals, the low-frequency content is the most important part.
The high-frequency content on the other hand imparts flavor.
In wavelet analysis, we often speak of approximations and details.
The approximations are the high-scale, low-frequency components of the signal. The details are the low-scale, high-frequency components.
Since the analysis process is iterative, in theory it can be continued indefinitely.
In reality, the decomposition can proceed only until the individual details consist of a single sample or pixel.
you’ll select a suitable number of levels based on the nature of the signal, or on a suitable criterion such as entropy.
Wavelet Reconstruction:
The discrete wavelet transform can be used to analyze or decompose signals and images. This process is called decomposition or analysis.
The other half of the story is how those components can be assembled back into the original signal without loss of information. This process is called reconstruction, or synthesis.
The mathematical manipulation that effects synthesis is called the inverse discrete wavelet transforms (IDWT).
To synthesize a signal in the Wavelet Toolbox, we reconstruct it from the wavelet coefficients:
Where wavelet analysis involves filtering and down sampling, the wavelet reconstruction process consists of up sampling and filtering.
Up sampling is the process of lengthening a signal component by inserting zeros between samples
Conclusion:
This paper investigates combination of multiple wavelets at feature level for palmprint based authentication system using an indigenously developed peg-free image acquisition platform.
The results depict the superiority of combined wavelets over individual wavelet feature for the palmprint authentication, using coarse level information. The paper also presented a new approach for rotation invariance, which proved its effectiveness by enhancing genuine accep-tance rate.
